Image evaluation

ABSTRACT

A machine may be configured to perform image evaluation of images depicting items for online publishing. For example, the machine performing a user behavior analysis based on data pertaining to interactions by a plurality of users with a plurality of images pertaining to a particular type of item. The machine determines, based on the user behavior analysis, that a presentation type associated with one or more images of the plurality of images corresponds to a user behavior in relation to the one or more images. The machine determines that an item included in a received image is of the particular type of item. The machine generates an output for display in a client device. The output includes a reference to the received image and a recommendation of the presentation type for the item included in the received image, for publication by a web server of a publication system.

CLAIM OF PRIORITY

This application claims the benefit of priority, under 35 U.S.C. Section119(e), to U.S. Provisional Patent Application No. 61/975,608 by Di etal., filed on Apr. 4, 2014, which is hereby incorporated herein byreference in its entirety.

This application is a continuation of and claims the benefit ofpriority, under 35 U.S.C. § 120, to U.S. patent application Ser. No.14/319,224 by Di et al., filed on Jun. 20, 2014, which is herebyincorporated herein by reference in its entirety.

TECHNICAL FIELD

The subject matter disclosed herein generally relates to the processingof data. Specifically, the present disclosure addresses systems andmethods to facilitate image evaluation.

BACKGROUND

Images depicting items for sale may be used to visually communicateinformation about the items for sale to potential buyers. For example,an online clothing store may use images to illustrate one or more itemsof merchandise available for purchase at the online clothing store. Animage may display an item of clothing modeled by a person, displayed ona mannequin, or displayed flat (e.g., with neither a human model nor amannequin).

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings.

FIG. 1 is a network diagram depicting a client-server system, withinwhich some example embodiments may be deployed.

FIG. 2 is a block diagram illustrating marketplace and paymentapplications and that, in some example embodiments, are provided as partof application server(s) 118 in the networked system.

FIG. 3 is a network diagram illustrating a network environment suitablefor image evaluation, according to some example embodiments.

FIG. 4 is a functional diagram of an example image evaluation machine,according to some example embodiments.

FIG. 5 is a block diagram illustrating components of the imageevaluation machine, according to some example embodiments.

FIGS. 6-14 are flowcharts illustrating operations of the imageevaluation machine in performing a method of evaluating one or moreimages, according to some example embodiments.

FIG. 15 is a block diagram illustrating a mobile device, according tosome example embodiments.

FIG. 16 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium and perform any one or more of the methodologiesdiscussed herein.

DETAILED DESCRIPTION

Example methods and systems for evaluation of images depicting items forsale online, to improve sales of the items, are provided. Examplesmerely typify possible variations. Unless explicitly stated otherwise,components and functions are optional and may be combined or subdivided,and operations may vary in sequence or be combined or subdivided. In thefollowing description, for purposes of explanation, numerous specificdetails are set forth to provide a thorough understanding of exampleembodiments. It will be evident to one skilled in the art, however, thatthe present subject matter may be practiced without these specificdetails.

Fashion (e.g., apparel) is a fast-growing category in online shopping.Because a user shopping in an online store may not have the same sensoryexperience as when shopping in a brick-and-mortar store, the use ofimages depicting the items (e.g., goods or services) for sale is veryimportant. Images of items, especially of apparel goods for which theirappearance matters to the buyers, play a key role in conveying crucialinformation about the goods that can be hard to express in text. Whethera user selects (e.g., clicks on) an image to visually examine the itemillustrated in the image may influence whether the user purchases theitem.

Because the visual representation of an item (e.g., an item of apparel)for sale may significantly impact the user's choices with respect to theparticular item (e.g., the initial research, the decision to examine theimage showing the item, the decision to mark the image for futurereference, or the purchase decision), it may be beneficial to a sellerto obtain an evaluation of the relative effectiveness of different typesof images in showcasing the item for sale. An evaluation of an imagedepicting an item for sale online may facilitate a presentation of theitem in an image in the best way such that the image serves as apersuasive tool that helps a seller of the item to sell the item.Furthermore, the evaluation of the image that depicts the item for salemay assist sellers in avoiding a possible mistaken conclusion on thepart of potential buyers that the item is of poor quality simply basedon the image of the item being of poor quality.

In some example embodiments, an image evaluation system may facilitatean image evaluation of an image depicting an item for sale. The imageevaluation of the image, in some example embodiments, may be based on animage analysis of the image and on an analysis of user behavior inrelation to (e.g., toward) images. According to certain exampleembodiments, the evaluation of an image may include an examination ofthe image based on a number of image attributes (e.g., a display typefor the item, professional photography, lighting, atmosphere, imagequality, or a suitable combination thereof), a classification of theimage into one or more categories based on one or more image attributes,a comparison of the image to other images submitted by other sellers,and/or a determination of the likelihood of obtaining a desired responsefrom a user (e.g., a potential buyer of the item) who sees the image.This evaluation may be based on a comparison of the image with images ofother similar items in similar categories, as well as compared withimages provided by other sellers.

The image evaluation may be used to provide feedback or recommendationsto the provider of images (e.g., a seller of the items for sale) withrespect to the images in order to increase the sales (e.g., improve thesale-through rate) of the items depicted in the images. The feedback mayinclude statements such as: “Your image of the item is 80% better thanthe images uploaded by other sellers of the item;” “Your image of theitem is better than the images provided by 40% of the sellers of thistype of item;” “Your image of the item would improve if you used a modelto display the item depicted in the image;” “Use better lighting;” “Usemore contrast in the image;” or “Use a different filter.”

According to various example embodiments, a recommendation may addressimproving the image in order to obtain a desired response from users(e.g., increase the click-through rate for the image or increase salesof the item). In some example embodiments, the image evaluation of animage submitted by (e.g., received from) a seller of an item depicted inthe image is based on the results of an analysis of the image submittedby the seller and on the results of an analysis of data that describesuser behavior in relation to images that depict items similar to theitem (e.g., belonging to the same category of clothing) depicted in theimage received from the seller. The data that describes user behavior inrelation to images may include indicators of actions taken by users(e.g., buyers) in response to seeing a plurality of images displayed tothe users. The data that describes user behavior in relation to images(also called “user behavior data” or “user behavior indicators”) may becaptured (e.g., collected) based on the interaction of one or more userswith numerous images that include a variety of display types and are ofvaried image quality (e.g., lighting, professional photographs, or asuitable combination thereof).

In some example embodiments, large scale user behavior data from aworldwide e-commerce platform may be used to evaluate the effect ofdifferent display types on people's shopping behaviors. Generally, inthe context of online apparel selling, clothing may be displayed inthree ways: on a human model, on a mannequin, or flat (e.g., withneither a mannequin nor a human model). Analyses of behavioral andtransaction data (e.g., clicks, watches, bookmarks, or purchases) revealthat users are more drawn to clothing that is modeled by a human modelthan clothing displayed on a mannequin or in a flat form, even whenother factors (e.g., the price, or the seller details) are accountedfor. In some example embodiments, the image evaluation system predicts,based on modeling user preferences, the level of attention of the usersto an image depicting an item (e.g., a clothing item). The imageevaluation system may also determine the likelihood of purchase of theitem based on the item presentation (e.g., one or more images depictingthe item) submitted by the seller of the item. In some instances, theimage evaluation system may recommend that a seller use a display typethat is more effective in attracting buyers' attention to the item andthat may increase the sell-through rate for the item.

FIG. 1 is a network diagram depicting a client-server system 100, withinwhich one example embodiment may be deployed. A networked system 102, inthe example forms of a network-based marketplace or publication system,provides server-side functionality, via a network 104 (e.g., theInternet or a Wide Area Network (WAN)), to one or more clients. FIG. 1illustrates, for example, a web client 106 (e.g., a browser, such as theInternet Explorer browser developed by Microsoft Corporation of Redmond,Wash. State) and a programmatic client 108 executing on respectivedevices 110 and 112.

An Application Program Interface (API) server 114 and a web server 116are coupled to, and provide programmatic and web interfaces respectivelyto, one or more application servers 118. The application servers 118host one or more marketplace applications 120 and payment applications122. The application servers 118 are, in turn, shown to be coupled toone or more database servers 124 that facilitate access to one or moredatabases 126.

The marketplace applications 120 may provide a number of marketplacefunctions and services to users who access the networked system 102. Invarious example embodiments, the marketplace applications 120 mayinclude an image evaluator 132. The image evaluator 132, in some exampleembodiments, may facilitate an image evaluation of an image depicting anitem for sale and a determination of the likelihood of obtaining adesired response from a user (e.g., a potential buyer of the item) whosees the image.

The payment applications 122 may likewise provide a number of paymentservices and functions to users. The payment applications 122 may allowusers to accumulate value (e.g., in a commercial currency, such as theU.S. dollar, or a proprietary currency, such as “points”) in accounts,and then later to redeem the accumulated value for products (e.g., goodsor services) that are made available via the marketplace applications120. While the marketplace and payment applications 120 and 122 areshown in FIG. 1 to both form part of the networked system 102, it willbe appreciated that, in alternative embodiments, the paymentapplications 122 may form part of a payment service that is separate anddistinct from the networked system 102.

Further, while the system 100 shown in FIG. 1 employs a client-serverarchitecture, the embodiments are, of course, not limited to such anarchitecture, and could equally well find application in a distributed,or peer-to-peer, architecture system, for example. The variousmarketplace and payment applications 120 and 122 could also beimplemented as standalone software programs, which do not necessarilyhave networking capabilities.

The web client 106 accesses the various marketplace and paymentapplications 120 and 122 via the web interface supported by the webserver 116. Similarly, the programmatic client 108 accesses the variousservices and functions provided by the marketplace and paymentapplications 120 and 122 via the programmatic interface provided by theAPI server 114. The programmatic client 108 may, for example, be aseller application (e.g., the TurboLister application developed by eBayInc., of San Jose, Calif.) to enable sellers to author and managelistings on the networked system 102 in an off-line manner, and toperform batch-mode communications between the programmatic client 108and the networked system 102.

FIG. 1 also illustrates a third party application 128, executing on athird party server machine 130, as having programmatic access to thenetworked system 102 via the programmatic interface provided by the APIserver 114. For example, the third party application 128 may, utilizinginformation retrieved from the networked system 102, support one or morefeatures or functions on a website hosted by the third party. The thirdparty website may, for example, provide one or more promotional,marketplace, or payment functions that are supported by the relevantapplications of the networked system 102.

FIG. 2 is a block diagram illustrating marketplace and paymentapplications 120 and 122 that, in one example embodiment, are providedas part of application server(s) 118 in the networked system 102. Theapplications 120 and 122 may be hosted on dedicated or shared servermachines (not shown) that are communicatively coupled to enablecommunications between server machines. The applications 120 and 122themselves are communicatively coupled (e.g., via appropriateinterfaces) to each other and to various data sources, so as to allowinformation to be passed between the applications 120 and 122 or so asto allow the applications 120 and 122 to share and access common data.The applications 120 and 122 may furthermore access one or moredatabases 126 via the database servers 124.

The networked system 102 may provide a number of publishing, listing,and price-setting mechanisms whereby a seller may list (or publishinformation concerning) goods or services for sale, a buyer can expressinterest in or indicate a desire to purchase such goods or services, anda price can be set for a transaction pertaining to the goods orservices. To this end, the marketplace and payment applications 120 and122 are shown to include at least one publication application 200 andone or more auction applications 202, which support auction-formatlisting and price setting mechanisms (e.g., English, Dutch, Vickrey,Chinese, Double, Reverse auctions, etc.). The various auctionapplications 202 may also provide a number of features in support ofsuch auction-format listings, such as a reserve price feature whereby aseller may specify a reserve price in connection with a listing and aproxy-bidding feature whereby a bidder may invoke automated proxybidding.

A number of fixed-price applications 204 support fixed-price listingformats (e.g., the traditional classified advertisement-type listing ora catalogue listing) and buyout-type listings. Specifically, buyout-typelistings (e.g., including the Buy-It-Now (BIN) technology developed byeBay Inc., of San Jose, Calif.) may be offered in conjunction withauction-format listings, and allow a buyer to purchase goods orservices, which are also being offered for sale via an auction, for afixed-price that is typically higher than the starting price of theauction.

Store applications 206 allow a seller to group listings within a“virtual” store, which may be branded and otherwise personalized by andfor the seller. Such a virtual store may also offer promotions,incentives, and features that are specific and personalized to arelevant seller.

Reputation applications 208 allow users who transact, utilizing thenetworked system 102, to establish, build, and maintain reputations,which may be made available and published to potential trading partners.Consider that where, for example, the networked system 102 supportsperson-to-person trading, users may otherwise have no history or otherreference information whereby the trustworthiness and credibility ofpotential trading partners may be assessed. The reputation applications208 allow a user (for example, through feedback provided by othertransaction partners) to establish a reputation within the networkedsystem 102 over time. Other potential trading partners may thenreference such a reputation for the purposes of assessing credibilityand trustworthiness.

Personalization applications 210 allow users of the networked system 102to personalize various aspects of their interactions with the networkedsystem 102. For example a user may, utilizing an appropriatepersonalization application 210, create a personalized reference page atwhich information regarding transactions to which the user is (or hasbeen) a party may be viewed. Further, a personalization application 210may enable a user to personalize listings and other aspects of theirinteractions with the networked system 102 and other parties.

The networked system 102 may support a number of marketplaces that arecustomized, for example, for specific geographic regions. A version ofthe networked system 102 may be customized for the United Kingdom,whereas another version of the networked system 102 may be customizedfor the United States. Each of these versions may operate as anindependent marketplace or may be customized (or internationalized)presentations of a common underlying marketplace. The networked system102 may accordingly include a number of internationalizationapplications 212 that customize information (and/or the presentation ofinformation by the networked system 102) according to predeterminedcriteria (e.g., geographic, demographic or marketplace criteria). Forexample, the internationalization applications 212 may be used tosupport the customization of information for a number of regionalwebsites that are operated by the networked system 102 and that areaccessible via respective web servers 116.

Navigation of the networked system 102 may be facilitated by one or morenavigation applications 214. For example, a search application (as anexample of a navigation application 214) may enable key word searches oflistings published via the networked system 102. A browse applicationmay allow users to browse various category, catalogue, or inventory datastructures according to which listings may be classified within thenetworked system 102. Various other navigation applications 214 may beprovided to supplement the search and browsing applications.

In order to make listings available via the networked system 102 asvisually informing and attractive as possible, the applications 120 and122 may include one or more imaging applications 216, which users mayutilize to upload images for inclusion within listings. An imagingapplication 216 also operates to incorporate images within viewedlistings. The imaging applications 216 may also support one or morepromotional features, such as image galleries that are presented topotential buyers. For example, sellers may pay an additional fee to havean image included within a gallery of images for promoted items.

Listing creation applications 218 allow sellers to conveniently authorlistings pertaining to goods or services that they wish to transact viathe networked system 102, and listing management applications 220 allowsellers to manage such listings. Specifically, where a particular sellerhas authored and/or published a large number of listings, the managementof such listings may present a challenge. The listing managementapplications 220 provide a number of features (e.g., auto-relisting,inventory level monitors, etc.) to assist the seller in managing suchlistings. One or more post-listing management applications 222 alsoassist sellers with a number of activities that typically occurpost-listing. For example, upon completion of an auction facilitated byone or more auction applications 202, a seller may wish to leavefeedback regarding a particular buyer. To this end, a post-listingmanagement application 222 may provide an interface to one or morereputation applications 208, so as to allow the seller conveniently toprovide feedback regarding multiple buyers to the reputationapplications 208.

Dispute resolution applications 224 provide mechanisms whereby disputesarising between transacting parties may be resolved. For example, thedispute resolution applications 224 may provide guided procedureswhereby the parties are guided through a number of steps in an attemptto settle a dispute. In the event that the dispute cannot be settled viathe guided procedures, the dispute may be escalated to a third partymediator or arbitrator.

A number of fraud prevention applications 226 implement fraud detectionand prevention mechanisms to reduce the occurrence of fraud within thenetworked system 102.

Messaging applications 228 are responsible for the generation anddelivery of messages to users of the networked system 102 (such as, forexample, messages advising users regarding the status of listings at thenetworked system 102 (e.g., providing “outbid” notices to bidders duringan auction process or to provide promotional and merchandisinginformation to users)). Respective messaging applications 228 mayutilize any one of a number of message delivery networks and platformsto deliver messages to users. For example, messaging applications 228may deliver electronic mail (e-mail), instant message (IM), ShortMessage Service (SMS), text, facsimile, or voice (e.g., Voice over IP(VoIP)) messages via the wired (e.g., the Internet), plain old telephoneservice (POTS), or wireless (e.g., mobile, cellular, WiFi, WiMAX)networks 104.

Merchandising applications 230 support various merchandising functionsthat are made available to sellers to enable sellers to increase salesvia the networked system 102. The merchandising applications 230 alsooperate the various merchandising features that may be invoked bysellers, and may monitor and track the success of merchandisingstrategies employed by sellers.

The networked system 102 itself, or one or more parties that transactvia the networked system 102, may operate loyalty programs that aresupported by one or more loyalty/promotions applications 232. Forexample, a buyer may earn loyalty or promotion points for eachtransaction established and/or concluded with a particular seller, andbe offered a reward for which accumulated loyalty points can beredeemed.

FIG. 3 is a network diagram illustrating a network environment 300suitable for image evaluation, according to some example embodiments.The network environment 300 includes an image evaluation machine 310(e.g., the image evaluator 132), a database 126, and devices 330 and350, all communicatively coupled to each other via a network 390. Theimage evaluation machine 310, with or without the database 126, may formall or part of a network-based system 305 (e.g., a cloud-based serversystem configured to provide one or more image processing services,image evaluation services, or both, to the devices 330 and 350). One orboth of the devices 330 and 350 may include a camera that allows captureof an image (e.g., an image of an item for sale). One or both of thedevices 330 and 350 may facilitate the communication of the image (e.g.,as a submission to the database 126) to the image evaluation machine310. The image evaluation machine 310 and the devices 330 and 350 mayeach be implemented in a computer system, in whole or in part, asdescribed below with respect to FIG. 16.

Also shown in FIG. 3 are users 332 and 352. One or both of the users 332and 352 may be a human user (e.g., a human being), a machine user (e.g.,a computer configured by a software program to interact with the device330), or any suitable combination thereof (e.g., a human assisted by amachine or a machine supervised by a human). The user 332 is not part ofthe network environment 300, but is associated with the device 330 andmay be a user of the device 330. For example, the device 330 may be adesktop computer, a vehicle computer, a tablet computer, a navigationaldevice, a portable media device, a smartphone, or a wearable device(e.g., a smart watch or smart glasses) belonging to the user 332.Likewise, the user 352 is not part of the network environment 300, butis associated with the device 350. As an example, the device 350 may bea desktop computer, a vehicle computer, a tablet computer, anavigational device, a portable media device, a smartphone, or awearable device (e.g., a smart watch or smart glasses) belonging to theuser 352.

Any of the machines, databases, or devices shown in FIG. 3 may beimplemented in a general-purpose computer modified (e.g., configured orprogrammed) by software (e.g., one or more software modules) to be aspecial-purpose computer to perform one or more of the functionsdescribed herein for that machine, database, or device. For example, acomputer system able to implement any one or more of the methodologiesdescribed herein is discussed below with respect to FIG. 16. As usedherein, a “database” is a data storage resource and may store datastructured as a text file, a table, a spreadsheet, a relational database(e.g., an object-relational database), a triple store, a hierarchicaldata store, or any suitable combination thereof. Moreover, any two ormore of the machines, databases, or devices illustrated in FIG. 3 may becombined into a single machine, and the functions described herein forany single machine, database, or device may be subdivided among multiplemachines, databases, or devices.

The network 390 may be any network that enables communication between oramong machines, databases, and devices (e.g., the server machine 310 andthe device 330). Accordingly, the network 390 may be a wired network, awireless network (e.g., a mobile or cellular network), or any suitablecombination thereof. The network 390 may include one or more portionsthat constitute a private network, a public network (e.g., theInternet), or any suitable combination thereof. Accordingly, the network390 may include one or more portions that incorporate a local areanetwork (LAN), a wide area network (WAN), the Internet, a mobiletelephone network (e.g., a cellular network), a wired telephone network(e.g., a plain old telephone system (POTS) network), a wireless datanetwork (e.g., WiFi network or WiMax network), or any suitablecombination thereof. Any one or more portions of the network 390 maycommunicate information via a transmission medium. As used herein,“transmission medium” refers to any intangible (e.g., transitory) mediumthat is capable of communicating (e.g., transmitting) instructions forexecution by a machine (e.g., by one or more processors of such amachine), and includes digital or analog communication signals or otherintangible media to facilitate communication of such software.

FIG. 4 is a functional diagram of an example image evaluation machine310, according to some example embodiments. In some example embodiments,the image evaluation machine 310 is included in a network-based system400. As described in more detail below, the image evaluation machine 310may receive an image 410 (e.g., an image of a clothing item). Thereceived image 410 (also “image 410”) may be received from the device330 associated with the user 332.

In response to receiving the image 410, the image evaluation machine 310analyses (e.g., performs an image analysis 420 of) the image using atleast one computer processor. In some example embodiments, to performthe image analysis 420, the image evaluation machine 310 extracts one ormore visual features from the received image 410. The image evaluationmachine 310 may also identify the value(s) of one or more imageattributes of the received image 410 based on the extracted one or morevisual features of the received image 410 and the image attribute data430 (e.g., data that identifies or describes image attributes and thevalues the image attributes may take). The image evaluation machine 310may also classify the received image 410 into one or more categoriesbased on the value(s) of the one or more image attributes 430.

For example, the one or more visual features extracted from the receivedimage 410 may include data that indicates a display type (e.g., a humanperson, a mannequin, or a flat display) used to display the clothingitem depicted in the received image 410. The image attribute data 430may include a number of image attributes, such as a display type,background, contrast, or lighting. Each image attribute 430 may beassociated with (e.g., take, or have a corresponding) one or morevalues. For instance, the display type attribute may take one of threevalues: (1) person, (2) mannequin, or (3) flat. Based on the one or morevisual features extracted from the received image 410 and the displaytype attribute, the image evaluation machine 310 may identify the valueof the display attribute of the received image 410 (e.g., person). Theimage evaluation machine 310 may classify the received image 410 into aspecific display type category (e.g., the person category, the mannequincategory, or the flat category) based on the identified value of thedisplay type attribute of the received image 410.

In some example embodiments, instead of or in addition to classifyingthe received image 410 into a category based on the display typeattribute value of the received image 410, the image evaluation machine310 assigns (e.g., attributes) a label (e.g., a tag) to the receivedimage 410 that identifies the display type used to display the itemdepicted in the image. The image evaluation machine 310 may also computea confidence score value for the received image 410 to indicate a levelof certainty of the correct determination of the display type of thereceived image 410. The label or the confidence score value, or both,may be stored in a record of a database (e.g., the database 126) inassociation with the received image 410, and may be used in furtherimage evaluation of the received image 410.

According to certain example embodiments, one or more image attributesand their values (e.g., one or more attribute-value pairs) serve asbasis for computing an image score value for an image. For example, fora particular image of a black dress the value of the attribute “displaytype” is identified to be “person”, the value of the attribute“lighting” is determined to be “good”, and the value of the attribute“background” is determined to be “white.” Based on these attribute-valuepairs, an image score value may be determined for the particular imageof the black dress. Other attributes, such as contrast, clarity,arrangement, composition, or balance, or a suitable combination ofattributes, may also serve as basis for computing the image score valueof an image that depicts an item.

In some example embodiments, the image score value of an image may bebased on a confidence score value of the image and a low-level-qualityscore value of the image. The image evaluation machine 310 may determinea confidence score attributable to (e.g., for, of, or assigned to) thereceived image 410 that measures a level of certainty that the receivedimage 410 is classified into a category to which the received image 410belongs (e.g., an image that uses the “person” display type belongs tothe person category). In certain example embodiments, the confidencescore value of the received image 410 takes a value between 0 and 1. Thehigher the confidence score value, the higher the certainty level thatthe image evaluation machine 310 determined correctly the category intowhich to classify the received image 410. For example, the imageevaluation machine 310 determines that an image A is of the “mannequin”display type with a confidence score value of 0.8. That may mean thatthere is an 80% certainty that the image evaluation machine 310determined the display type of the image A correctly. The imageevaluation machine 310 may determine a low-level-quality score valueattributable to (e.g., for, of, or assigned to) the received image 410based on one or more other image attributes (e.g., lighting, clarity, orprofessionally-generated) of the received image 410. In certain exampleembodiments, the confidence score value of the received image 410 andthe low-level-quality score value of the received image 410 are combinedto generate an image score value of the received image 410.

The confidence score value of the received image 410 and thelow-level-quality score value of the received image 410 may be combined,in some instances, based on multiplying the confidence score value ofthe received image 410 and the low-level-quality score value of thereceived image 410 to compute the image score value of the receivedimage 410. In other instances, the confidence score value of thereceived image 410 and the low-level-quality score value of the receivedimage 410 may be assigned particular weights, according to a weightassigning rule, to generate a weighted confidence score value of thereceived image 410 and a weighted low-level-quality score value of thereceived image 410. The weighted confidence score value of the receivedimage 410 and the weighted low-level-quality score value of the receivedimage 410 may be added together to compute the image score value of thereceived image 410. In some example embodiments, the particular weightsmay be selected during the user behavior analysis 450 of user behaviordata 440 that indicates how users, who see a plurality of images thatdepict similar items and have different image attribute values, act inrelation to particular images.

In some example embodiments, the image evaluation machine 310 ranks theimages included in a particular category of images (e.g., the imagesdepicting clothing in the person category) or a sub-category of aparticular category of images (e.g., the images depicting black dressesin the person category). The ranking may be based on the confidencescore values of the images, the low-level-quality score values of theimages, or the image score value that combines the confidence scorevalue and the low-level-score value of the respective images. Forexample, the image evaluation machine 310 identifies the images includedin the person category of images that depict clothing items displayedusing human persons. The image evaluation machine 310 determines theconfidence score values and the low-level-quality score values of therespective images in the person category. For each image in the personcategory, the image evaluation machine 310 may combine the confidencescore value and the low-level-quality value that correspond to aparticular image to generate the image score value that corresponds tothe particular image. Upon computing the image score values for theimages in the person category, the image evaluation machine 310 may rankthe images within the person category based on their respective imagescore values. In some example embodiments, the images are presented tousers (e.g., buyers) according to their image score value.

According to various example embodiments, the image evaluation machine310 collects (e.g., captures, accesses, or receives) user behavior data440 (e.g., indicators of actions taken by potential buyers, actualbuyers, or a combination thereof, in relation to a plurality of imagesthat depict clothing items of a particular type). In some exampleembodiments, the user behavior data 440 represents the behavior of oneor more users searching for an item and responding to (e.g., selectingor not selecting, or viewing or not viewing) one or more images thatdepict the searched item and that are displayed to the one or moreusers. The user behavior data 440, in some example embodiments, iscollected over a period of time. In other example embodiments, the userbehavior data 440 is collected at one instance in time.

The image evaluation machine 310 may perform a user behavior analysis450 based on the user behavior data 440 to learn (e.g., determine) whatimages are preferred by the users (e.g., buyers). In particular, theimage evaluation machine 310 may determine what image attribute-valuepairs may correlate to desirable user behavior (e.g., clicking on theimage or purchasing the item depicted in the image). For example, theuser behavior data 440 may be collected from an e-commerce platform(e.g., an online marketplace, an online store, or a web site). Thee-commerce platform may enable users to search for items for sale usinga text query entered at a web site. In each search session, a user mayinput a query looking for a certain item and the search engine mayreturn multiple search results that reference the items (e.g., imagesthat depict the items). Searches may be very personalized tasks withhuge variations in terms of search intention and product attributes. Inorder to focus only on apparel items and limit the images to similarcontent (e.g., the same category of product), the image evaluationmachine 310 may limit the scope of user behavior data collected.

In some example embodiments, the image evaluation machine 310 may beconfigured to be a query-dependent machine. As a query-dependentmachine, the image evaluation machine 310 may be configured to collectonly descriptors of (e.g., data that describes) query sessions that usea specific keyword. For example, the image evaluation machine 310 may beconfigured to collect only descriptors of query sessions using thekeyword “black dress.” In another example, the image evaluation machine310 may be configured to collect only descriptors of query sessionsusing the keyword “sweaters.” By configuring the image evaluationmachine 310 to be a query-dependent machine, the image evaluationmachine 310 may limit the scope of user behavior data collected based ona specific query.

The image evaluation machine 310 may rank the search results referencingthe items (e.g., the images depicting a black dress) based on theirrelevance to a particular keyword. In some example embodiments, whencollecting the user behavior data 440, the image evaluation machine 310may collect only data that describes user behavior in relation to imagesof highly relevant items. The highly relevant items may be the itemsdepicted in images displayed on the first search results page inresponse to a query entered by a user.

Accordingly, the images that are part of the search results are likelyto have the same content (e.g., a black dress). The images may onlydiffer in their presentation (e.g., image attributes). For example, theimages may differ in the display type of the black dress: on a humanmodel, mannequin, or just flat. It may be beneficial to sellers to knowhow users behave in relation to images that have similar content butdifferent image attributes.

The performing of the user behavior analysis 450 may indicate how usersbehave when presented with a number of images that have different imageattributes. In some instances, the performing of the user behavioranalysis 450 may facilitate a determination of whether the image ispleasant to buyers, whether the image is likely to help sell the itemshown in the image, or whether the image is likely to be ignored bypotential buyers of the item depicted in the image. In some exampleembodiments, the performing of the user behavior analysis 450 mayinclude identifying specific user actions by one or more users inrelation to one or more images that depict an item of clothing (e.g., ablack dress) and that manifest the one or more users' interest towardthe image or the item depicted in the image. A user's interest toward aparticular image or the item depicted in the particular image may, insome instances, be implied based on the user's interaction with theparticular image. The specific user actions may include, for example,selecting (e.g., clicking on) a particular image from a plurality ofimages displayed to the user, bookmarking the particular image for laterreference, emailing the image, printing the image, viewing the image fora time exceeding a threshold period of time, or purchasing the itemdepicted in the image within another threshold period of time afterviewing the image. The performing of the user behavior analysis 450 mayalso include identifying one or more attributes of the one or moreimages that depict the item of clothing and determining whether the oneor more attributes correlate to an increased number ofinterest-manifesting activities by the one or more users to the one ormore images.

In some example embodiments, the user behavior analysis 450 may focus onanswering the following questions with respect to the display typeattribute: (1) Do the three styles (e.g., person, mannequin, or flat)differ in their likelihood of being clicked on a search results page?(2) Do the three styles differ by motivating users to bookmark or“watch” the item? (3) Do the three styles differ in increasing thesell-through rate? (4) Quantitatively, how much difference is therebetween user preferences for these three styles? Other or additionalquestions may be used when examining the influence of other imageattributes on user shopping behavior.

For a successful transaction, it may be crucial to attract the attentionof potential consumers. User interest may be shown at different stagesduring the process of online shopping (e.g. browsing, click action, orpurchase). A user-choice model for quantifying user preferences betweenthe person-, mannequin-, and flat-display styles (also called the“PMF-user choice model” or the “PMF model”) may facilitate theunderstanding and the quantifying of user responses at three stagesduring the online purchasing circle: (a) a “Click” action at the searchresult page, where multiple relevant items may be displayed according tothe search query; (b) a “Watch” action at the view item page, whereshoppers may evaluate the item in greater detail and may make decisionsto either put the item on hold (e.g., by watching), to continue tobrowse, or to purchase the item; or (c) a “Purchase” action where theuser makes a final decision on the product.

TABLE 1 Distribution shift for displayed, clicked, and unclicked items.For clicked items, the proportion of P-type increases while theproportions of M-type and F-type decrease indicating users favor P-typeover M-type or F-type. Type Displayed Items Clicked Items UnclickedItems Flat 40.87% 39.21% 40.99% Mannequin 34.49% 33.26% 34.57% Person24.65% 27.53% 24.44%

Given multiple relevant items displayed on the search result page, auser click response at the search result page may be identified (e.g.,by the image evaluation machine 310). By categorizing image content intoPMF types, Table 1 above shows a significant distribution shift from theoriginal displayed search result to what were clicked by the users. Theratio of Person-type (also “P-type”) is only 24.65% for displayed items,but increases to 27.53% for clicked items. Proportions decrease for bothMannequin-type (also “M-type”) and Flat-type (also “F-type”) for theclicked items. This distribution indicates that users favor P-typedisplays over M-type or F-type. Buyers show a strong inclination towarditems presented in P-type even for different price segments or sellertype.

Given higher attention drawn by P-type on the search result page, theimage evaluation machine 310 may identify user actions on the view itempage. The view item page may be a page where buyers may obtain detailsabout the item depicted in the image and may engage in otherinterest-manifesting actions (e.g., bookmark the item for a more seriousevaluation) indicating more direct shopping intention. The imageevaluation machine 310 may compute the average watch count for each PMFtype and for each seller group. The results shown in Table 2 belowsuggest a positive correlation of the “watch action” with top seller aswell as P-type product presentation. For items sold by either casual ortop seller, a P-type image helps increase the chance of being watched.The proportion of P-type images goes up for highly watched items ascompared to less watched items.

TABLE 2 Average “Watch Count” for each display type with respect toseller types. Results suggest P-type is correlated with higher averagewatch rate for both casual and top seller. Avg-Watch Type Casual-SellerTop-seller Flat 1.48 1.89 Mannequin 1.89 2.32 Person 2.73 3.32

The sell-through rate may be the ultimate evaluation metric for anonline listing. Table 3 lists the conversion rate of each display typegrouped by click action observed in the collected session data. Comparedto unclicked items, clicked items show higher conversion rate, which maybe expected because users show interest in the item through clicking onan image that depicts the item, which may lead to a higher chance ofpurchase. A comparison of the three display types (e.g., person,mannequin, and flat) may show that the items displayed in P-typedemonstrate a better sell-through rate for either clicked or unclickeditems.

TABLE 3 Conversion Rate for three display types for clicked andunclicked items in the collected session data, where items displayed byP-type show better sell- through rate. Clicked Items Unclicked ItemsFlat 41.88% 26.05% Mannequin 42.45% 23.46% Person 47.94% 28.23%

The image evaluation machine 310 may also use the PMF-user choice modelto determine the difference between the preferences for each displaystyle and to quantitatively compare them. In the PMF-user choice model,W_(i) ⊂f m, p may denote the level of preference toward each type,F_(i)⊂f, m, p may be the proportions of each type in the original set ofretrieved items where F_(f)+F_(m)+F_(p)=1, and P_(i)⊂f, m, p may be theproportion of each type among the clicked items. The smaller theproportion F_(i) is, the harder it shows up in search result and pickedby user. The higher preference W_(i) is, the more likely this given typemay be selected. Thus, P_(i) is affected by both factors: distributionbias represented by F_(i) and preference bias W_(i).

Different approaches to merging these two factors, to obtain areasonable prediction of a user preference level, may exist. In someexample embodiments, only two choices are weighed, and it may be assumedthat the ratio of the difficulty in the selecting given type isinversely proportional to the ratio of their post-click-distributions,where the difficulty is modeled by both F_(i) and W_(i). It also may beassumed that given equal preference (e.g., W_(f)=W_(m)=W_(p)) thereshould be no significant shift from before-click-distribution topost-click-distribution. In other words, it may be expected thatF_(i)=P_(i). Based on this idea, a PMF User Choice model one (C1) may beproposed:

$\begin{matrix}{\frac{\frac{1}{F_{i}} + \frac{1}{W_{i}} - {\frac{1}{3} \times {\sum\limits_{{t \Subset f},m,p}\;\frac{1}{W_{t}}}}}{\frac{1}{F_{j}} + \frac{1}{W_{j}} - {\frac{1}{3} \times {\sum\limits_{{t \Subset f},m,p}\;\frac{1}{W_{t}}}}} = \frac{P_{j}}{P_{i}}} & (1)\end{matrix}$where

${\frac{1}{W_{i}} - {\frac{1}{3} \times {\sum\limits_{{t \Subset f},m,p}\;\frac{1}{W_{t}}}}} = 0$when the level of all three types are identical, resulting in the samePMF distributions for both before-click and post-click data. Given thesame constraint, another model may be to use multiplication instead,which leads to model two (C2):

$\begin{matrix}{\frac{\frac{1}{F_{i}} \times \frac{1}{W_{i}}}{\frac{1}{F_{j}} \times \frac{1}{W_{j}}} = \frac{P_{j}}{P_{i}}} & (2)\end{matrix}$

The solution to both models is a set of pair-wise relations betweenpreference levels, where W_(f) and W_(m) are paramertized as a functionof W_(p). By taking input from Table 1, i.e. assign F_(f)=0.4087,F_(m)=0.3449 and F_(p)=0.2465 as before-click-distribution, andP_(f)=0.3921, P_(m)=0.3326 and P_(p)=0.2753 as post-click-distribution,model C1 generates:

$\begin{matrix}{{W_{m}^{C\; 1} = \frac{W_{p}}{{0.51789 \times W_{p}} + 1}}{W_{f}^{C\; 1} = \frac{W_{p}}{{0.50304 \times W_{p}} + 1}}} & (3)\end{matrix}$

The result of model C2 shows directly that preference of M-type is about86.3% of the preference to P-type:W _(m) ^(C) ² =0.863×W _(p)W _(f) ^(C) ² =0.859×W _(p)  (4)

Table 4 lists predicted preference level from both models, subject tosum-to-one, in accordance with an example embodiment. The P-type gainsthe highest preference, whereas no significant difference is foundbetween M-type and F-type. There are two possible reasons. First, theFlat category consists of many non-dress items, which are retrieved whenuser uses queries like “black dress shoes.” E-shoppers may tend to clickthose items either because they are exploring, or searching forcoordinate items (e.g., shoes or belts) that match well with a blackdress. Second, because Mannequin is an inanimate human-size figure, itis not as pleasant-looking to viewers as an actual human being.

TABLE 4 Estimated preference level of each PMF type by two proposed PMFUser Choice models, where M or F-type is about 86% of P-type. Type C1 C2Flat 0.3147 0.3155 Mannequin 0.3133 0.3171 Person 0.3740 0.3673

The results of the user behavior analysis 450 described above may showthat, in some example embodiments, the P-type display of clothing inimages may be the most effective product presentation among the threedisplay types, and may help the most to attract users' attention andraise the sell-through rate. The results of the user behavior analysis450 may be useful to numerous recipients, for example, to apparele-retailers in choosing a better presentation strategy or to e-commerceoperators in designing a better search or feed recommendation system toimprove click-through rates. Furthermore, in some example embodiments,the user behavior analysis 450 may be used to evaluate an image thatdepicts an item to determine the likelihood of the image to elicit adesired response from a potential buyer of the item.

For example, the user behavior analysis 450 and the image analysis 420may be used to perform an image evaluation 460 of the received image 410received from the device 130 of the user 132 (e.g., a seller). In someexample embodiments, the image evaluation machine 310 may compute, basedon the results of the image analysis 420 and the results of the userbehavior analysis 450, the likelihood of an average buyer engaging in aninterest-manifesting action in relation to (e.g., towards) the receivedimage 410 or the item depicted in the received image 410. Examples ofinterest-manifesting action in relation the received image 410 or theitem illustrated in the received image 410 are selecting (e.g., clickingon) the received image 410 depicting the item or purchasing the itemupon viewing the received image 410. According to certain exampleembodiments, the image evaluation machine 310 determines an image scorevalue for the received image 410 that may indicate to the user 132 howan average buyer may respond to the received image 410 (e.g., what thelikelihood of the buyer clicking on the received image 410 is or whatthe likelihood of the buyer purchasing the item depicted in the imageis).

According to some example embodiments, upon performing the imageevaluation 460, the image evaluation machine 310 may generate an output470 that may be communicated to the user 132 via the device 130.Examples of the output 470 include a report of the results of the imageevaluation 460, feedback based on the results of the image evaluation460, or a recommendation how to improve the presentation (e.g., thedisplay) of the item included in the received image 410. The output 470may, in some instances, include the image score value for the receivedimage 410, a ranking of the received image 410 as compared to otherimages (e.g., within a category of images or regardless of the categoryof images) provided by other sellers, or an indication of the likelihoodof selling the item depicted in the received image 410 if using thereceived image 410 as a representation of the item on an e-commercesite.

In some example embodiments, the output 470 includes a suggestion on howto select a cost-effective display type based on the results of theimage analysis 420, the image evaluation 460, or both. For example, theoutput 470 may include the image score value for the received image 410or the ranking of the received image 410 as compared to other images(e.g., within a category of images or regardless of the category ofimages) provided by other sellers, and may provide one or more optionsto improve the display of the item using images that depicts the itembased on the image score value, the ranking value (e.g., position,order, or score), or both. In some instances, one of the options may beto choose a different type of display (e.g., choose M-type over F-type,or P-type over M-type) where the change of display type iscost-effective. In other instances, where the cost of changing thedisplay type is high, the suggested option may be to improve otherattributes of the image (e.g., lighting, professional photography, or anuncluttered or white background).

FIG. 5 is a block diagram illustrating components of the imageevaluation machine 310, according to some example embodiments. The imageevaluation machine 310 is shown as including a receiver module 510, animage analysis module 520, a behavior analysis module 530, an outputmodule 540, and a communication module 550, all configured tocommunicate with each other (e.g., via a bus, shared memory, or aswitch).

Any one or more of the modules described herein may be implemented usinghardware (e.g., one or more processors of a machine) or a combination ofhardware and software. For example, any module described herein mayconfigure a processor (e.g., among one or more processors of a machine)to perform the operations described herein for that module. Moreover,any two or more of these modules may be combined into a single module,and the functions described herein for a single module may be subdividedamong multiple modules. Furthermore, according to various exampleembodiments, modules described herein as being implemented within asingle machine, database, or device may be distributed across multiplemachines, databases, or devices.

FIGS. 6-14 are flowcharts illustrating operations of the imageevaluation machine 310 in performing a method 600 of evaluating one ormore images, according to some example embodiments. Operations in themethod 600 may be performed using modules described above with respectto FIG. 5. As shown in FIG. 6, the method 600 may include one or more ofoperations 610, 620, 630, and 640.

Image evaluation by the image evaluation machine 310 may begin, atmethod operation 610, with the receiver module 510 accessing one or moreresults of a user behavior analysis. The results of the user behavioranalysis may be generated by the behavior analysis module 530 based onanalyzing the user behavior data 440. The user behavior data 440 mayrelate to a plurality of test images. The user behavior data 440 may becollected based on user behavior of one or more users (e.g., potentialbuyers of actual buyers) in relation to the plurality of test images.The analysis of the user behavior data 440 may include determining userpreferences of the one or more users for particular value(s) of one ormore image attributes of a plurality of images (e.g., images included ina library of test images) displayed to the plurality of users.

At method operation 620, the receiver module 510 receives an image of anitem. The image may be received from a user device (e.g., a smartphone)of a user (e.g., a seller). The image, in some example embodiments, maydepict an item of clothing.

At method operation 630, the image analysis module 520 performs, usingone or more processors, an image evaluation of the image. The performingof the image evaluation of the image may include performing an imageanalysis of the image and evaluating the image based on the imageanalysis of the image. The evaluating of the image may be based on aresult of the image analysis and a result of the user behavior analysisaccessed by the receiver module 510. In some example embodiments, theevaluating of the image includes determining a likelihood of obtaining adesired response from one or more buyers to whom the image may bedisplayed.

At method operation 640, the output module 540 generates an output thatreferences (e.g., includes a reference to, identifies using anidentifier (ID), etc.) the image. The output may be generated based onthe image evaluation of the image. The output may be generated for theuser device in response to receiving the image of the item from the userdevice.

In some example embodiments, the method 600 may further comprisetransmitting a communication to the device of the seller (e.g., by thecommunication module 550). The communication may include the generatedoutput that references the image. Further details with respect to themethod operations of the method 600 are described below with respect toFIGS. 6A-14.

As shown in FIG. 6A, the method 600 may include one or more of methodoperations 631, 632, and 633, according to some example embodiments.Method operation 631 may be performed as part (e.g., a precursor task, asubroutine, or a portion) of the method operation 630, in which theoutput module 540 generates an output that references the image. Atmethod operation 631, the image analysis module 520 computes a scorevalue for the received image. The computing of the score value for theimage may be based on the values of the one or more image attributes ofthe image.

Method operation 632 may be performed after method operation 631. Atmethod operation 632, the image analysis module 520 determines alikelihood of a different user (e.g., a buyer) engaging in a desireduser behavior in relation to (e.g., toward) the received image. Examplesof desired user behavior in relation to the received image areselecting, clicking on, or marking for future reference the receivedimage; purchasing the item depicted in the received image or placing therespective item on a wish list; etc. In some instances, the receivedimage may be received from a seller of the item depicted in the receivedimage, from an agent of the seller, or from a user device of the selleror of the agent of the seller. In some example embodiments, thedetermining of the likelihood of the different user engaging in adesired user behavior in relation to the received image may be based onthe score value of the received image. In various example embodiments,the determining of the likelihood of the different user engaging in adesired user behavior in relation to the received image may be based onthe one or more results of the analysis of the user behavior data. Incertain example embodiments, the determining of the likelihood of thedifferent user engaging in a desired user behavior in relation to thereceived image may be based on the score value of the received image andon the one or more results of the analysis of the user behavior data.

Method operation 633 may be performed as part (e.g., a precursor task, asubroutine, or a portion) of method of method operation 640, in whichthe output module 540 generates an output that references the image. Atmethod operation 633, the output module 540 generates an output thatreferences the image and indicates the likelihood of the user engagingin a desired user behavior in relation to the received image.

As shown in FIG. 6B, the method 600 may include one or more of methodoperations 634, 634, 636, and 637, according to some exampleembodiments. Method operation 634 may be performed after methodoperation 632, in which the image analysis module 520 determines alikelihood of a user (e.g., a generic buyer, a specific buyer, etc.)engaging in a desired user behavior in relation to the received image.At method operation 634, the image analysis module 520 accesses value(s)of one or more image attributed of another (e.g., a second) image ofanother similar item. The item depicted in the image and the othersimilar item depicted in the other image may have similarcharacteristics, such as style, color, pattern, etc. For example, boththe item and the other similar item may be little black dresses.

Method operation 635 may be performed after method operation 634. Atmethod operation 635, image analysis module 520 computes a second scorevalue corresponding to the other image. The computing of the secondscore value may be based on the values of the one or more imageattributes of the other image.

Method operation 636 may be performed after method operation 635. Atmethod operation 636, image analysis module 520 compares the image withthe other image based on the score value (e.g., a first score value)corresponding to the image and the second score value corresponding tothe other image.

Method operation 637 may be performed after method operation 636. Atmethod operation 637, image analysis module 520 generates a ranking ofthe image of the item relative to the other image of the other similaritem.

As shown in FIG. 7, the method 600 may include one or more of methodoperations 701, 702, 703, and 704, according to some exampleembodiments. Method operation 701 may be performed before the methodoperation 610, in which the receiver module 510 accesses one or moreresults of a user behavior analysis. At method operation 701, thebehavior analysis module 530 accesses (e.g., receives) user behaviordata. The user behavior data may be indicators of actions taken by users(e.g., actual or potential buyers) in response to receiving a number ofimages that depict one or more similar items, indicators of lack ofaction taken by the users, or a combination thereof.

Method operation 702 may be performed before the method operation 610,in which the receiver module 510 accesses one or more results of a userbehavior analysis. At method operation 702, the behavior analysis module530 analyzes the user behavior data, as described above with respect toFIG. 2.

Method operation 703 may be performed before the method operation 610,in which the receiver module 510 accesses one or more results of a userbehavior analysis. At method operation 703, the behavior analysis module530 generates one or more results of the analysis of the user behaviordata. In some example embodiments, the one or more results of theanalysis of the user behavior data includes one or more indicators ofuser preferences for particular images of particular image attributes.For example, an indicator of user preferences indicates that users(e.g., apparel buyers) generally prefer images that use the persondisplay type to display clothing items. According to another example, anindicator of user preferences indicates that, with images that use themannequin display type, a majority of users prefer images that have awhite background. The users' preferences for particular images may beimplied based on user actions toward particular images, such asselecting (e.g., clicking on) the particular images or marking the imagefor future reference. In some instances, the users' preferences for theparticular images may be implied based on the users purchasing the itemsdepicted in the particular images.

Method operation 704 may be performed before the method operation 610,in which the receiver module 510 accesses one or more results of a userbehavior analysis. At method operation 704, the behavior analysis module530 stores the results of the analysis of the user behavior data in adatabase (e.g., the database 126).

As shown in FIG. 8, the method 600 may include one or more of methodoperations 801, 802, and 803, according to some example embodiments.Method operation 801 may be performed as part (e.g., a precursor task, asubroutine, or a portion) of method operation 630, in which the imageanalysis module 520 performs an image evaluation of the image. At methodoperation 801, the image analysis module 520 extracts one or more visualfeatures from the image received from the seller.

Method operation 802 may be performed after method operation 801. Atmethod operation 802, the image analysis module 520 identifies thevalues of the one or more image attributes of the image (e.g., the valueof a display type used to display the item within the image). Theidentifying of the display type used to display the item within theimage may be based on the one or more visual features extracted from theimage received from the seller. In some example embodiments, the methodoperations 801 and 802 are performed as part of the image analysis 420discussed above with respect to FIG. 4.

Method operation 803 may be performed after method operation 802. Atmethod operation 803, the image analysis module 520 determines alikelihood of a user (e.g., a buyer), who sees the image, engaging in aninterest-manifesting action in relation to the image (e.g., clicking onthe image or purchasing the item depicted in the image). The determiningof the likelihood of the buyer, who sees the image, engaging in aninterest-manifesting action in relation to the image, may be based onthe image analysis (or a result of the image analysis, e.g., the valuesof the one or more image attributes of the image, such as the identifiedvalue of display type of the image) and the user behavior data (e.g., aresult of user behavior analysis). According to some exampleembodiments, the user behavior data includes data that identifiesinteractions by potential buyers of the item with one or more imagesillustrating the item. The buyer behavior data may indicate, in variousexample embodiments, a preference by the potential buyers in selectingimages of a particular display type of a plurality of display types.Examples of interest-manifesting actions or interactions with images areselecting or clicking the image, viewing the image, placing the itemdepicted in the image on a wish list, pinning the image, or purchasingthe item depicted in the image.

As shown in FIG. 9, the method 600 may include one or more of methodoperations 801, 802, 901, and 902, according to some exampleembodiments. Method operation 801, as described with respect to FIG. 8,may be performed as part (e.g., a precursor task, a subroutine, or aportion) of method operation 630, in which the image analysis module 520performs an image evaluation of the image. At method operation 801, theimage analysis module 520 extracts one or more visual features from theimage received from the seller.

Method operation 802, as described with respect to FIG. 8, may beperformed after the method operation 801. At method operation 802, theimage analysis module 520 identifies a display type used to display theitem within the image. The identifying of the display type used todisplay the item within the image may be based on the one or more visualfeatures extracted from the image received from the seller. In someexample embodiments, the method operations 801 and 802 are performed aspart of the image analysis 420 discussed above with respect to FIG. 4.

Method operation 901 may be performed after method operation 802. Atmethod operation 901, the image analysis module 520 determines an imagescore value for the image. The image score value for the image may bedetermined based on the display type used to display the item within theimage.

Method operation 902 may be performed after the method operation 901. Atmethod operation 902, the image analysis module 520 determines alikelihood of a buyer, who sees the image, engaging in aninterest-manifesting action in relation to (e.g., clicking on) theimage. The determining of the likelihood of the buyer, who sees theimage, engaging in an interest-manifesting action in relation to theimage, may be based on the image analysis (or a result of the imageanalysis, e.g., the image score value of the image) and the userbehavior data (e.g., a result of user behavior analysis).

As shown in FIG. 10, the method 600 may include method operation 1001.Method operation 1001 may be performed as part (e.g., a precursor task,a subroutine, or a portion) of method operation 630, in which the imageanalysis module 520 performs an image evaluation of the image. At methodoperation 1001, the image analysis module 520 determines that the imageis better than a different image. The determining that the image isbetter than a different image may be based on a comparison of the image(e.g., a first image received from a first seller) and a different image(e.g., a second image received from the first seller or a second imagereceived from a different seller).

In certain example embodiments, to compare the image and a differentimage, the image analysis module 520, using one or more attributecomparison rules, performs a comparison of one or more imageattributes-value pairs of the image and one or more corresponding imageattributes-value pairs of the different image. For example, the imageanalysis module 520 may identify the respective values of the “clarity”attribute of a first image and of a second image. Based on applying anattribute comparison rule that specifies which attribute valuecorresponding to the “clarity” attribute ranks higher, the imageanalysis module 520 may determine which of the first and second imageshas a higher ranking value for the “clarity” attribute. Accordingly, theimage analysis module 520 may identify the image that has the higherranking value for the “clarity” attribute as the better image.

According to a different example, the image analysis module 520 mayidentify the respective values of the “display type” attribute of afirst image and of a second image. Based on applying an attributecomparison rule that specifies which attribute value corresponding tothe “display type” attribute ranks higher, the image analysis module 520may determine which of the first and second images has a higher rankingvalue for the “display type” attribute. The attribute comparison rulethat specifies which attribute value (e.g., “person”, “mannequin”, or“flat”) corresponding to the “display type” attribute ranks higher(e.g., “person” ranks higher than “mannequin” or “mannequin” rankshigher than “flat”) may be generated during the analysis of the userbehavior data. Accordingly, the image analysis module 520 may identifythe image that has the higher ranking value for the “display type”attribute as the better image.

As shown in FIG. 11, the method 600 may include one or more of methodoperations 1101, 1102, and 1103, according to some example embodiments.Method operation 1101 may be performed as part (e.g., a precursor task,a subroutine, or a portion) of method operation 1001, in which the imageanalysis module 520 determines that the image is better than a differentimage. At method operation 1101, the image analysis module 520determines that the image is better than a different image based on theimage being of a person display type (e.g., the item depicted in theimage is displayed using a human model).

Method operation 1102 may be performed as part (e.g., a precursor task,a subroutine, or a portion) of method operation 1001, in which the imageanalysis module 520 determines that the image is better than a differentimage. At method operation 1102, the image analysis module 520determines that the image is better than a different image based on theimage being of a mannequin display type (e.g., the item depicted in theimage is displayed using a mannequin).

Method operation 1103 may be performed as part (e.g., a precursor task,a subroutine, or a portion) of method operation 1001, in which the imageanalysis module 520 determines that the image is better than a differentimage. At method operation 1103, the image analysis module 520determines that the image is better than a different image based on theimage being of a flat display type (e.g., the item depicted in the imageis displayed flat, without using a human model or a mannequin).

As shown in FIG. 12, the method 600 may include method operation 1201.Method operation 1201 may be performed as part (e.g., a precursor task,a subroutine, or a portion) of method operation 1001, in which the imageanalysis module 520 determines that the image is better than a differentimage. At method operation 1201, the image analysis module 520determines that the image is better than a different image based on theimage having a higher rank value than the different image. The imageanalysis module 520 may rank a plurality of images received from users(e.g., sellers) according to one or more ranking rules. The result ofthe ranking may identify a particular order of images.

In some example embodiments, the image and the different image may beranked within a category of images to which the image and the differentimage belong based on their corresponding value of the display typeattribute, as described above with respect to FIG. 4. For example, ifthe image and the different image both use the person display type todisplay the items depicted in the images, the image and different imagemay be ranked within the person category of images. The images within acategory may, in some example embodiments, be ranked based on the imagescore values of the images within the particular category.

In some example embodiments, the image and the different image may beranked globally (e.g., ranked regardless of categories to which theimages may belong) based on an image appeal score value. In some exampleembodiments, the image analysis module 520 determines the image appealscore value of an image using a particular formula based on theclassification of the image.

For example, the user preference for each PMF is P, M, and F (correspondto the 0.37, 0.31, 0.31 values in Table 4 above). The confidence scorevalue is C. If the image is classified as P, the formula is:P×C+M×(1−C)/2+F×(1−C)/2.

Similarly, if the image is classified as M, the formula is:P×(1−C)/2+M×C+F×(1−C)/2.

Similarly, if the image is classified as F, the formula is:P×(1−C)/2+M×(1−C)/2+F×C.

For example, based on an analysis of the user behavior data, thereference score values for images of different display types may be asfollows: P=0.37, M=0.31, and F=0.31. If a given image A is classified asP-type, with the confidence score value of 0.7, then the image appealscore value of the image A equalsP×C+M×(1−C)/2+F×(1−C)/2=0.37×0.7+0.31×(1−0.7)/2+0.31×(1−0.7)/2.

The image appeal score value of the image A may be further combined withthe low-level-quality score value of the image A to compute a finalscore value. In some example embodiments, the final score value is usedto determine a global ranking of a plurality of images.

The combining of the image appeal score value and of thelow-level-quality-score value may include, in some instances,multiplying the image appeal score value of the image A and thelow-level-quality-score value of the image A to compute the final scorevalue of the image A. The combining of the image appeal score value ofthe image A and of the low-level-quality-score value of the image A mayinclude, in some instances, assigning particular weights to the imageappeal score value of the image A and to the low-level-quality-scorevalue of the image A, according to a weight assigning rule, to generatea weighted image appeal score value of the image A and a weightedlow-level-quality-score value of the image A. The combining may furtherinclude adding the weighted image appeal score value of the image A andthe weighted low-level-quality-score value of the image A to compute thefinal score value of the image A. In some example embodiments, theparticular weights may be selected during the analysis of the userbehavior data. Some or all of the scores' values (e.g., the confidencescore value, the image score value, or the final score value) attributedto an image may be stored in one or more records of a database (e.g.,the database 126).

As shown in FIG. 13, the method 600 may include one or more of themethod operations 1301, 1302, and 1303, according to some exampleembodiments. Method operation 1301 may be performed as part (e.g., aprecursor task, a subroutine, or a portion) of method operation 1001, inwhich the image analysis module 520 determines that the image is betterthan a different image. At method operation 1301, the image analysismodule 520 determines that the image is better than a different imagebased on the image having a higher rank value than the different image,the image and the different image being of a person display type.

Method operation 1302 may be performed as part (e.g., a precursor task,a subroutine, or a portion) of method operation 1001, in which the imageanalysis module 520 determines that the image is better than a differentimage. At method operation 1302, the image analysis module 520determines that the image is better than a different image based on theimage having a higher rank value than the different image, the image andthe different image being of a mannequin display type.

Method operation 1303 may be performed as part (e.g., a precursor task,a subroutine, or a portion) of method operation 1001, in which the imageanalysis module 520 determines that the image is better than a differentimage. At method operation 1303, the image analysis module 520determines that the image is better than a different image based on theimage having a higher rank value than the different image, the image andthe different image being of a flat display type.

As shown in FIG. 14, the method 600 may include one or more of methodoperations 1401, 1402, and 1403, according to some example embodiments.Method operation 1401 may be performed as part (e.g., a precursor task,a subroutine, or a portion) of method operation 640, in which the outputmodule 540 generates an output that references the image. At methodoperation 1401, the output module 540 generates feedback that referencesthe image. The feedback may include a result of the image evaluation ofthe image (e.g., an evaluation of the image to determine the likelihoodof a user, who sees the image, engaging in an interest-manifestingaction in relation to the image or the item), an explanation of theimage evaluation results, a report of the evaluation of the image, acomparison of a plurality of images received from the seller, acomparison of the image submitted by the seller and the images submittedby other sellers (e.g., based on the image score values or the imageranking values of the respective images), examples of good or badimages, or a suitable combination thereof.

Method operation 1402 may be performed as part (e.g., a precursor task,a subroutine, or a portion) of method operation 640, in which the outputmodule 540 generates an output that references the image. At methodoperation 1402, the output module 540 generates a recommendation thatreferences the image. The recommendation may include a suggestion for animproved image of the item. The improved image of the item may increasea likelihood of obtaining a desired result from a user (e.g., apotential buyer), such as engaging in an interest-manifesting activityin relation to the image or the item depicted in the image (e.g.,clicking on the image or buying the item). The suggestion, in someinstances, may include a description of changes that may be made to anumber of characteristics (e.g., image attributes) of the image receivedfrom the seller.

The recommendation may include a result of the image evaluation of theimage (e.g., an evaluation of the image to determine the likelihood of auser, who sees the image, engaging in an interest-manifesting action inrelation to the image or the item), an explanation of the imageevaluation results, a comparison of a plurality of images received fromthe seller, a report of the evaluation of the image, a comparison of theimage submitted by the seller and the images submitted by other sellers(e.g., based on the image score values or the image ranking value of therespective images), a suggestion to select a more effective display typeto display the item in the image, a suggestion to modify the values ofone or more other image attributes (e.g., better lighting, a whitebackground, professional photography, fewer items shown in the image, ora better image composition), a set of guidelines to assist the seller inmaking a decision on how to improve the image depicting the item (e.g.,a cost-benefit analysis of different image improving options), examplesof good or bad images, or a suitable combination thereof.

Method operation 1403 may be performed as part (e.g., a precursor task,a subroutine, or a portion) of method operation 640, in which the outputmodule 540 generates an output that references the image. At methodoperation 1403, the output module 540 generates a set of guidelines thatassist sellers in selecting images that are likely to result in desiredresponses from buyers. In some example embodiments, the set ofguidelines may describe suggestions how to generate or select qualityimages that may facilitate the increase in sales of the items depictedin the images. The guidelines may be provided (e.g., displayed) to thesellers of the items, for example, at an e-commerce site where thesellers may market or sell their items. In some example embodiments, theguidelines are provided to the sellers before the sellers transmit(e.g., upload) images to the e-commerce site. In certain exampleembodiments, the guidelines are provided to the sellers after thesellers transmit (e.g., upload) images to the e-commerce site. The setof guidelines may be customized for particular sellers based on theresults of the evaluation of the images received from the particularsellers. For example, upon the image analysis module 520 completing theimage evaluation of an image received from a seller and determining thatthe image may require improvement, the output module 540 generates acustomized set of guidelines that may assist the seller in selecting animage that is likely to result in desired responses from buyers. Thecommunication module 550 may display the set of guidelines to the sellervia a device of the seller.

In certain example embodiments, the output module 540 may determine,based on the image score value of the image received from the seller,what type of output to generate. For example, the image analysis module520 may determine, based on the extracted visual features of the imagethat the image utilizes the P-type display to depict the item ofclothing. Based on the image utilizing the P-type display, the imageanalysis module 520 may assign a high image score value to the image (ascompared to other images that utilize the M-type or F-type). The outputmodule 540 may determine, based on the image score value of the image,that the output that references the image may include feedback thatreferences the image (for instance, feedback with respect to how theimage compares (e.g., positively) to other images submitted by othersellers) but may not include a recommendation how to improve the imagebased on the image already having a high image score value.

According to various example embodiments, one or more of themethodologies described herein may facilitate the evaluation of imagesdepicting items for sale online. Moreover, one or more of themethodologies described herein may facilitate providing recommendationsfor improving the images depicting the items for sale online. Hence, oneor more the methodologies described herein may facilitate improvingsales of the items depicted in the images.

When these effects are considered in aggregate, one or more of themethodologies described herein may obviate a need for certain efforts orresources that otherwise would be involved in evaluating images of itemsfor sale online. Efforts expended by a provider of such images (e.g.,the seller) in evaluating such images may be reduced by one or more ofthe methodologies described herein. Computing resources used by one ormore machines, databases, or devices (e.g., within the networkenvironment 300) may similarly be reduced. Examples of such computingresources include processor cycles, network traffic, memory usage, datastorage capacity, power consumption, and cooling capacity.

Example Mobile Device

FIG. 15 is a block diagram illustrating a mobile device 1500, accordingto an example embodiment. The mobile device 1500 may include a processor1502. The processor 1502 may be any of a variety of different types ofcommercially available processors 1502 suitable for mobile devices 1500(for example, an XScale architecture microprocessor, a microprocessorwithout interlocked pipeline stages (MIPS) architecture processor, oranother type of processor 1502). A memory 1504, such as a random accessmemory (RAM), a flash memory, or other type of memory, is typicallyaccessible to the processor 1502. The memory 1504 may be adapted tostore an operating system (OS) 1506, as well as application programs1508, such as a mobile location enabled application that may provide LBSs to a user. The processor 1502 may be coupled, either directly or viaappropriate intermediary hardware, to a display 1510 and to one or moreinput/output (I/O) devices 1512, such as a keypad, a touch panel sensor,a microphone, and the like. Similarly, in some embodiments, theprocessor 1502 may be coupled to a transceiver 1514 that interfaces withan antenna 1516. The transceiver 1514 may be configured to both transmitand receive cellular network signals, wireless data signals, or othertypes of signals via the antenna 1516, depending on the nature of themobile device 1500. Further, in some configurations, a GPS receiver 1518may also make use of the antenna 1516 to receive GPS signals.

Modules, Components and Logic

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied (1) on a non-transitorymachine-readable medium or (2) in a transmission signal) orhardware-implemented modules. A hardware-implemented module is atangible unit capable of performing certain operations and may beconfigured or arranged in a certain manner. In example embodiments, oneor more computer systems (e.g., a standalone, client or server computersystem) or one or more processors 1502 may be configured by software(e.g., an application or application portion) as a hardware-implementedmodule that operates to perform certain operations as described herein.

In various embodiments, a hardware-implemented module may be implementedmechanically or electronically. For example, a hardware-implementedmodule may comprise dedicated circuitry or logic that is permanentlyconfigured (e.g., as a special-purpose processor, such as a fieldprogrammable gate array (FPGA) or an application-specific integratedcircuit (ASIC)) to perform certain operations. A hardware-implementedmodule may also comprise programmable logic or circuitry (e.g., asencompassed within a general-purpose processor 1502 or otherprogrammable processor 1502) that is temporarily configured by softwareto perform certain operations. It will be appreciated that the decisionto implement a hardware-implemented module mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software) may be driven by cost and timeconsiderations.

Accordingly, the term “hardware-implemented module” should be understoodto encompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired) or temporarily ortransitorily configured (e.g., programmed) to operate in a certainmanner and/or to perform certain operations described herein.Considering embodiments in which hardware-implemented modules aretemporarily configured (e.g., programmed), each of thehardware-implemented modules need not be configured or instantiated atany one instance in time. For example, where the hardware-implementedmodules comprise a general-purpose processor 1502 configured usingsoftware, the general-purpose processor 1502 may be configured asrespective different hardware-implemented modules at different times.Software may accordingly configure a processor 1502, for example, toconstitute a particular hardware-implemented module at one instance oftime and to constitute a different hardware-implemented module at adifferent instance of time.

Hardware-implemented modules can provide information to, and receiveinformation from, other hardware-implemented modules. Accordingly, thedescribed hardware-implemented modules may be regarded as beingcommunicatively coupled. Where multiple of such hardware-implementedmodules exist contemporaneously, communications may be achieved throughsignal transmission (e.g., over appropriate circuits and buses thatconnect the hardware-implemented modules). In embodiments in whichmultiple hardware-implemented modules are configured or instantiated atdifferent times, communications between such hardware-implementedmodules may be achieved, for example, through the storage and retrievalof information in memory structures to which the multiplehardware-implemented modules have access. For example, onehardware-implemented module may perform an operation, and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware-implemented module may then,at a later time, access the memory device to retrieve and process thestored output. Hardware-implemented modules may also initiatecommunications with input or output devices, and can operate on aresource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors 1502 that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors 1502 may constitute processor-implementedmodules that operate to perform one or more operations or functions. Themodules referred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods described herein may be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod may be performed by one or more processors 1502 orprocessor-implemented modules. The performance of certain of theoperations may be distributed among the one or more processors 1502 orprocessor-implemented modules, not only residing within a singlemachine, but deployed across a number of machines. In some exampleembodiments, the one or more processors 1502 or processor-implementedmodules may be located in a single location (e.g., within a homeenvironment, an office environment or as a server farm), while in otherembodiments the one or more processors 1502 or processor-implementedmodules may be distributed across a number of locations.

The one or more processors 1502 may also operate to support performanceof the relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), these operations being accessible via anetwork (e.g., the Internet) and via one or more appropriate interfaces(e.g., application program interfaces (APIs)).

Electronic Apparatus and System

Example embodiments may be implemented in digital electronic circuitry,or in computer hardware, firmware, software, or in combinations of them.Example embodiments may be implemented using a computer program product,e.g., a computer program tangibly embodied in an information carrier,e.g., in a machine-readable medium for execution by, or to control theoperation of, data processing apparatus, e.g., a programmable processor1502, a computer, or multiple computers.

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a module, subroutine,or other unit suitable for use in a computing environment. A computerprogram can be deployed to be executed on one computer or on multiplecomputers at one site or distributed across multiple sites andinterconnected by a communication network.

In example embodiments, operations may be performed by one or moreprogrammable processors 1502 executing a computer program to performfunctions by operating on input data and generating output. Methodoperations can also be performed by, and apparatus of exampleembodiments may be implemented as, special purpose logic circuitry,e.g., a field programmable gate array (FPGA) or an application-specificintegrated circuit (ASIC).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. Inembodiments deploying a programmable computing system, it will beappreciated that that both hardware and software architectures requireconsideration. Specifically, it will be appreciated that the choice ofwhether to implement certain functionality in permanently configuredhardware (e.g., an ASIC), in temporarily configured hardware (e.g., acombination of software and a programmable processor 1502), or acombination of permanently and temporarily configured hardware may be adesign choice. Below are set out hardware (e.g., machine) and softwarearchitectures that may be deployed, in various example embodiments.

Example Machine Architecture and Machine-Readable Medium

FIG. 16 is a block diagram illustrating components of a machine 1600,according to some example embodiments, able to read instructions 1624from a machine-readable medium 1622 (e.g., a non-transitorymachine-readable medium, a machine-readable storage medium, acomputer-readable storage medium, or any suitable combination thereof)and perform any one or more of the methodologies discussed herein, inwhole or in part. Specifically, FIG. 16 shows the machine 1600 in theexample form of a computer system (e.g., a computer) within which theinstructions 1624 (e.g., software, a program, an application, an applet,an app, or other executable code) for causing the machine 1600 toperform any one or more of the methodologies discussed herein may beexecuted, in whole or in part.

In alternative embodiments, the machine 1600 operates as a standalonedevice or may be connected (e.g., networked) to other machines. In anetworked deployment, the machine 1600 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a distributed (e.g., peer-to-peer)network environment. The machine 1600 may be a server computer, a clientcomputer, a personal computer (PC), a tablet computer, a laptopcomputer, a netbook, a cellular telephone, a smartphone, a set-top box(STB), a personal digital assistant (PDA), a web appliance, a networkrouter, a network switch, a network bridge, or any machine capable ofexecuting the instructions 1624, sequentially or otherwise, that specifyactions to be taken by that machine. Further, while only a singlemachine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executethe instructions 1624 to perform all or part of any one or more of themethodologies discussed herein.

The machine 1600 includes a processor 1602 (e.g., a central processingunit (CPU), a graphics processing unit (GPU), a digital signal processor(DSP), an application specific integrated circuit (ASIC), aradio-frequency integrated circuit (RFIC), or any suitable combinationthereof), a main memory 1604, and a static memory 1606, which areconfigured to communicate with each other via a bus 1608. The processor1602 may contain microcircuits that are configurable, temporarily orpermanently, by some or all of the instructions 1624 such that theprocessor 1602 is configurable to perform any one or more of themethodologies described herein, in whole or in part. For example, a setof one or more microcircuits of the processor 1602 may be configurableto execute one or more modules (e.g., software modules) describedherein.

The machine 1600 may further include a graphics display 1610 (e.g., aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, a cathode ray tube (CRT), orany other display capable of displaying graphics or video). The machine1600 may also include an alphanumeric input device 1612 (e.g., akeyboard or keypad), a cursor control device 1614 (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, an eye trackingdevice, or other pointing instrument), a storage unit 1616, an audiogeneration device 1618 (e.g., a sound card, an amplifier, a speaker, aheadphone jack, or any suitable combination thereof), and a networkinterface device 1620.

The storage unit 1616 includes the machine-readable medium 1622 (e.g., atangible and non-transitory machine-readable storage medium) on whichare stored the instructions 1624 embodying any one or more of themethodologies or functions described herein. The instructions 1624 mayalso reside, completely or at least partially, within the main memory1604, within the processor 1602 (e.g., within the processor's cachememory), or both, before or during execution thereof by the machine1600. Accordingly, the main memory 1604 and the processor 1602 may beconsidered machine-readable media (e.g., tangible and non-transitorymachine-readable media). The instructions 1624 may be transmitted orreceived over the network 1626 via the network interface device 1620.For example, the network interface device 1620 may communicate theinstructions 1624 using any one or more transfer protocols (e.g.,hypertext transfer protocol (HTTP)).

In some example embodiments, the machine 1600 may be a portablecomputing device, such as a smart phone or tablet computer, and have oneor more additional input components 1630 (e.g., sensors or gauges).Examples of such input components 1630 include an image input component(e.g., one or more cameras), an audio input component (e.g., amicrophone), a direction input component (e.g., a compass), a locationinput component (e.g., a global positioning system (GPS) receiver), anorientation component (e.g., a gyroscope), a motion detection component(e.g., one or more accelerometers), an altitude detection component(e.g., an altimeter), and a gas detection component (e.g., a gassensor). Inputs harvested by any one or more of these input componentsmay be accessible and available for use by any of the modules describedherein.

As used herein, the term “memory” refers to a machine-readable mediumable to store data temporarily or permanently and may be taken toinclude, but not be limited to, random-access memory (RAM), read-onlymemory (ROM), buffer memory, flash memory, and cache memory. While themachine-readable medium 1622 is shown in an example embodiment to be asingle medium, the term “machine-readable medium” should be taken toinclude a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storeinstructions. The term “machine-readable medium” shall also be taken toinclude any medium, or combination of multiple media, that is capable ofstoring the instructions 1624 for execution by the machine 1600, suchthat the instructions 1624, when executed by one or more processors ofthe machine 1600 (e.g., processor 1602), cause the machine 1600 toperform any one or more of the methodologies described herein, in wholeor in part. Accordingly, a “machine-readable medium” refers to a singlestorage apparatus or device, as well as cloud-based storage systems orstorage networks that include multiple storage apparatus or devices. Theterm “machine-readable medium” shall accordingly be taken to include,but not be limited to, one or more tangible (e.g., non-transitory) datarepositories in the form of a solid-state memory, an optical medium, amagnetic medium, or any suitable combination thereof.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute softwaremodules (e.g., code stored or otherwise embodied on a machine-readablemedium or in a transmission medium), hardware modules, or any suitablecombination thereof. A “hardware module” is a tangible (e.g.,non-transitory) unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware modules of a computer system (e.g., a processor or a groupof processors) may be configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

In some embodiments, a hardware module may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware module may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware module may be a special-purpose processor, such as a fieldprogrammable gate array (FPGA) or an ASIC. A hardware module may alsoinclude programmable logic or circuitry that is temporarily configuredby software to perform certain operations. For example, a hardwaremodule may include software encompassed within a general-purposeprocessor or other programmable processor. It will be appreciated thatthe decision to implement a hardware module mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software) may be driven by cost and timeconsiderations.

Accordingly, the phrase “hardware module” should be understood toencompass a tangible entity, and such a tangible entity may bephysically constructed, permanently configured (e.g., hardwired), ortemporarily configured (e.g., programmed) to operate in a certain manneror to perform certain operations described herein. As used herein,“hardware-implemented module” refers to a hardware module. Consideringembodiments in which hardware modules are temporarily configured (e.g.,programmed), each of the hardware modules need not be configured orinstantiated at any one instance in time. For example, where a hardwaremodule comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware modules) at different times. Software(e.g., a software module) may accordingly configure one or moreprocessors, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multiplehardware modules exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware modules. In embodiments inwhich multiple hardware modules are configured or instantiated atdifferent times, communications between such hardware modules may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware modules have access.For example, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented module” refers to ahardware module implemented using one or more processors.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, a processor being an example of hardware. Forexample, at least some of the operations of a method may be performed byone or more processors or processor-implemented modules. As used herein,“processor-implemented module” refers to a hardware module in which thehardware includes one or more processors. Moreover, the one or moreprocessors may also operate to support performance of the relevantoperations in a “cloud computing” environment or as a “software as aservice” (SaaS). For example, at least some of the operations may beperformed by a group of computers (as examples of machines includingprocessors), with these operations being accessible via a network (e.g.,the Internet) and via one or more appropriate interfaces (e.g., anapplication program interface (API)).

The performance of certain operations may be distributed among the oneor more processors, not only residing within a single machine, butdeployed across a number of machines. In some example embodiments, theone or more processors or processor-implemented modules may be locatedin a single geographic location (e.g., within a home environment, anoffice environment, or a server farm). In other example embodiments, theone or more processors or processor-implemented modules may bedistributed across a number of geographic locations.

Some portions of the subject matter discussed herein may be presented interms of algorithms or symbolic representations of operations on datastored as bits or binary digital signals within a machine memory (e.g.,a computer memory). Such algorithms or symbolic representations areexamples of techniques used by those of ordinary skill in the dataprocessing arts to convey the substance of their work to others skilledin the art. As used herein, an “algorithm” is a self-consistent sequenceof operations or similar processing leading to a desired result. In thiscontext, algorithms and operations involve physical manipulation ofphysical quantities. Typically, but not necessarily, such quantities maytake the form of electrical, magnetic, or optical signals capable ofbeing stored, accessed, transferred, combined, compared, or otherwisemanipulated by a machine. It is convenient at times, principally forreasons of common usage, to refer to such signals using words such as“data,” “content,” “bits,” “values,” “elements,” “symbols,”“characters,” “terms,” “numbers,” “numerals,” or the like. These words,however, are merely convenient labels and are to be associated withappropriate physical quantities.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or any suitable combination thereof), registers, orother machine components that receive, store, transmit, or displayinformation. Furthermore, unless specifically stated otherwise, theterms “a” or “an” are herein used, as is common in patent documents, toinclude one or more than one instance. Finally, as used herein, theconjunction “or” refers to a non-exclusive “or,” unless specificallystated otherwise.

What is claimed is:
 1. A system comprising: a non-transitorymachine-readable medium for storing instructions that, when executed byone or more hardware processors of a machine, cause the one or morehardware processors to perform operations comprising: accessing userbehavior data pertaining to interactions by a plurality of users with aplurality of test images pertaining to a particular type of item;performing an analysis of the interactions; generating results of theanalysis of the interactions, the results indicating a recommendedpresentation type for the particular type of item; storing the resultsof the analysis of the interactions in a database; receiving an image ofan item from a client device; extracting one or more visual featuresfrom the received image; determining that the item included in thereceived image is of the particular type of item based on the extractedone or more visual features; and generating, based on the determiningthat the item included in the received image is of the particular typeof item, an output for display in the client device, the outputincluding a reference to the received image and the recommendedpresentation type for the item included in the received image of theitem based on the results of the analysis of the interactions, whereinthe presentation type corresponds to an attribute-value pair of one ormore attribute-value pairs associated with one or more images of theplurality of test images corresponding to the user behavior, and whereinthe operations further comprise: determining, for the received image, alikelihood of a user engaging in the user behavior in relation to thereceived image based on one of the one or more attribute-value pairsassociated with the received image.
 2. The system of claim 1, whereinthe operations further comprise: determining a likelihood of a userengaging in the user behavior based on a publication, by the web serverof the publishing system, of an image of the particular type of itemusing the presentation type, wherein the output further includes areference to the likelihood of a user engaging in the user behavior. 3.The system of claim 1, wherein the operations further comprise:performing an evaluation of the received image based on comparing thereceived image and one or more other images.
 4. The system of claim 3,wherein the received image and the one or more other images are receivedfrom a client device associated with a particular user.
 5. The system ofclaim 3, wherein the received image is received from a first clientdevice associated with a first user and the one or more other images arereceived from a second client device associated with one or more otherusers.
 6. The system of claim 3, wherein the output includes a result ofthe comparing of the received image and the one or more other images. 7.The system of claim 1, wherein the operations further comprise:performing an evaluation of the received image based on an imageanalysis of the received image, the image analysis including identifyingone or more image attributes and one or more values that correspond tothe one or more image attributes, the one or more image attributescomprising at least one of an image quality attribute and a display typeattribute, the display type attribute indicating a visual context inwhich the item is displayed.
 8. The system of claim 7, wherein thedetermining that the item included in the received image is of theparticular type of item includes classifying the received image into acategory based on the one or more image attributes and the one or morevalues that correspond to the one or more image attributes.
 9. Thesystem of claim 7, wherein the generating of the output is further basedon a result of the evaluation of the received image.
 10. The system ofclaim 7, wherein the output includes a further recommendation to providea further image with a different value corresponding to an attribute ofthe one or more attributes.
 11. The system of claim 1, wherein theoutput includes a further recommendation of an action pertaining to thereceived image to increase the likelihood of the user engaging in theuser behavior.
 12. A method comprising: accessing user behavior datapertaining to interactions by a plurality of users with a plurality oftest images pertaining to a particular type of item; performing ananalysis of the interactions; generating results of the analysis of theinteractions, the results indicating a recommended presentation type forthe particular type of item; storing the results of the analysis of theinteractions in a database; receiving an image of an item from a clientdevice; extracting one or more visual features from the received image;determining, using one or more hardware processors, that the itemincluded in the received image is of the particular type of item basedon the extracted one or more visual features; and generating, based onthe determining that the item included in the received image is of theparticular type of item, an output for display in the client device, theoutput including a reference to the received image and the recommendedpresentation type for the item included in the received image of theitem based on the results of the analysis of the interactions whereinthe presentation type corresponds to an attribute-value pair of one ormore attribute-value pairs associated with one or more images of theplurality of test images corresponding to the user behavior, the methodfurther comprising: determining, for the received image, a likelihood ofa user engaging in the user behavior hi relation to the received imagebased on one of the one or more attribute-value pairs associated withthe received image.
 13. The method of claim 12, further comprising:determining a likelihood of a user engaging in the user behavior basedon a publication, by the web server of the publishing system, of animage of the particular type of item using the presentation type,wherein the output further includes a reference to the likelihood of auser engaging in the user behavior.
 14. The method of claim 12, furthercomprising: performing an evaluation of the received image based oncomparing the received image and one or more other images.
 15. Themethod of claim 12, further comprising: performing an evaluation of thereceived image based on an image analysis of the received image, theimage analysis including identifying one or more image attributes andone or more values that correspond to the one or more image attributes,the one or more image attributes comprising at least one of an imagequality attribute and a display type attribute, the display typeattribute indicating a visual context in which the item is displayed.16. A non-transitory computer-readable storage medium storinginstructions which, when executed by one or more hardware processors ofa machine, cause the machine to perform operations comprising:performing a user behavior analysis based on historical data pertainingto interactions by a plurality of users with a plurality of imagespertaining to a particular type of item; determining, based on the userbehavior analysis, that a presentation type associated with one or moreimages of the plurality of images corresponds to a user behavior inrelation to the one or more images; receiving an image of an item from aclient device; extracting one or more visual features from the receivedimage; determining that the item included in the received image is ofthe particular type of item based on the extracted one or more visualfeatures; and generating, based on the determining that the itemincluded in the received image is of the particular type of item, anoutput for display in the client device, the output including areference to the received image and the recommended presentation typefor the item included in the received image of the item based on theresults of the analysis of the interactions, wherein the presentationtype corresponds to an attribute-value pair of one or moreattribute-value pairs associated with the one or more imagescorresponding to the user behavior, and wherein the operations furthercomprising: determining, for the received image, a likelihood of a userengaging in the user behavior in relation to the received image based onone of the one or more attribute-value pairs associated with thereceived image.